Chapter 1. 1. Chapter. 1, . Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010. . Traditional Media. Broadcast Media: One-to-Many. Communication Media: One-to-One. ID: 756371
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Social Media and Social Computing
Chapter 1
1
Chapter
1,
Community Detection and Mining in Social Media. Lei Tang and Huan Liu, Morgan & Claypool, September, 2010.
Slide2Traditional Media
Broadcast Media: One-to-Many
Communication Media: One-to-One
2
Slide3Social Media:
Many-to-Many
3
Slide4Various forms of Social Media
Blog
: Wordpress, blogspot,
LiveJournal
Forum
: Yahoo! Answers,
Epinions
Media Sharing:
Flickr, YouTube, Scribd
Microblogging: Twitter, FourSquare
Social Networking: Facebook, LinkedIn,
OrkutSocial Bookmarking: Del.icio.us
, DiigoWikis: Wikipedia, scholarpedia
, AskDrWiki4
Slide5Characteristics of Social Media
“Consumers” become “Producers”
Rich User InteractionUser-Generated ContentsCollaborative environment
Collective Wisdom
Long Tail
Broadcast Media
Filter, then Publish
Social Media
Publish, then Filter
5
Slide6Top 20 Websites at USA
1
Google.com
11
Blogger.com
2
Facebook.com
12
msn.com
3
Yahoo.com
13
Myspace.com
4
YouTube.com
14
Go.com
5
Amazon.com
15
Bing.com
6
Wikipedia.org
16
AOL.com
7
Craigslist.org
17
LinkedIn.com
8
Twitter.com
18
CNN.com
9
Ebay.com
19
Espn.go.com
10
Live.com
20
Wordpress.com
40% of websites are social media sites
6
Slide77
Slide88
Slide9Networks and Representation
Graph Representation
Matrix Representation
9
Social Network
: A social structure made of nodes (individuals or organizations) and edges that connect nodes in various relationships like friendship, kinship etc.
Slide10Basic Concepts
A: the adjacency matrix
V: the set of nodesE: the set of edgesvi: a node v
i
e(v
i
,
v
j): an edge between node vi
and vjN
i: the neighborhood of node vi
di: the degree
of node vigeodesic: a shortest path between two nodes
geodesic distance10
Slide11Properties of Large-Scale Networks
Networks in social media are typically huge, involving millions of actors and connections.
Large-scale networks in real world demonstrate similar patternsScale-free distributions
Small-world effect
Strong Community Structure
11
Slide12Scale-free Distributions
Degree distribution in large-scale networks often follows a
power law.
A.k.a.
long tail
distribution,
scale-free
distribution
12
Slide13log-log plot
Power law distribution becomes a
straight line if plot in a log-log scale
13
Friendship Network in
Flickr
Friendship Network in YouTube
Slide14Small-World Effect
“
Six Degrees of Separation”A famous experiment conducted by Travers and
Milgram
(1969)
Subjects were asked to send a chain letter to his acquaintance in order to reach a target person
The average path length is around
5.5
Verified on a planetary-scale IM network of 180 million users (
Leskovec and Horvitz 2008)
The average path length is 6.6
14
Slide15Diameter
Measures used to calibrate the small world effect
Diameter: the longest shortest path in a network
Average shortest path length
15
The shortest path between two nodes is called
geodesic.
The number of hops in the geodesic is the
geodesic distance.
The geodesic distance between node 1 and node 9 is 4.
The diameter of the network is 5, corresponding to the geodesic distance between nodes 2 and 9.
Slide16Community Structure
Community
: People in a group interact with each other more frequently than those outside the groupFriends of a friend are likely to be friends as well
Measured by
clustering coefficient:
density of connections among one’s friends
16
Slide17Clustering Coefficient
d
6=4, N6= {4, 5, 7,8}
k
6
=4 as e(4,5), e(5,7), e(5,8), e(7,8)
C
6
= 4/(4*3/2) = 2/3
Average clustering coefficientC = (C1 + C
2 + … + C
n)/n
C = 0.61 for the left networkIn a random graph, the expected coefficient is 14/(9*8/2) = 0.19.
17
Slide18Challenges
Scalability
Social networks are often in a scale of millions of nodes and connectionsTraditional Network Analysis often deals with at most hundreds of subjects
Heterogeneity
Various types of entities and interactions are involved
Evolution
Timeliness is emphasized in social media
Collective Intelligence
How to utilize wisdom of crowds in forms of tags, wikis, reviews
EvaluationLack of ground truth, and complete information due to privacy
18
Slide19Social Computing Tasks
Social Computing: a young and vibrant field
Many new challengesTasksNetwork ModelingCentrality Analysis and Influence Modeling
Community Detection
Classification and Recommendation
Privacy, Spam and Security
19
Slide20Network Modeling
Large Networks demonstrate statistical patterns:
Small-world effect (e.g., 6 degrees of separation)Power-law distribution (a.k.a. scale-free distribution)
Community structure (high clustering coefficient)
M
odel the network dynamics
Find a mechanism such that the statistical patterns observed in large-scale networks can be reproduced.
Examples: random graph, preferential attachment process, Watts and
Strogatz
modelUsed for simulation to understand network propertiesThomas
Shelling’s famous
simulation: What could cause the segregation of white and black peopleNetwork robustness under attack
Slide21Comparing Network Models
observations over various
real-word large-scale networks
outcome of a
network model
(Figures borrowed from “
Emergence of Scaling in Random Networks
”)
Slide22Centrality Analysis and Influence Modeling
Centrality Analysis:
Identify the most important actors or edges
Various criteria
Influence modeling:
How is information diffused?
How does one influence each other?
Related Problems
Viral marketing: word-of-mouth effect
Influence maximization
22
Slide23Community Detection
A
community is a set of nodes between which the interactions are (relatively) frequent
A.k.a.,
group, cluster, cohesive subgroups, modules
Applications:
Recommendation based communities, Network Compression, Visualization of a huge network
New lines of research in social media
Community Detection in Heterogeneous Networks
Community Evolution in Dynamic Networks
Scalable Community Detection in Large-Scale Networks
23
Slide24Classification and Recommendation
Common in social media applications
Tag suggestion, Friend/Group Recommendation, Targeting
24
Link
prediction
Network-Based Classification
Slide25Privacy, Spam and Security
Privacy is a big concern in social media
Facebook, Google buzz often appear in debates about privacyNetFlix
Prize Sequel cancelled due to privacy concern
Simple
annoymization
does not necessarily protect privacy
Spam blog (
splog), spam comments, Fake identity, etc., all requires new techniques
As private information is involved, a secure and trustable system is critical Need to achieve a balance between sharing and privacy
25
Slide26Book Available at
Morgan & claypool Publishers
Amazon
If you have any comments,
please feel free to contact
:
Lei Tang
, Yahoo! Labs,
ltang@yahoo-inc.com
Huan
Liu
, ASU
huanliu@asu.edu
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